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Index C p , 78, 205, 206, 210–213 R 2 , 68–71, 79–80, 103, 212 l 2 norm, 216 l 1 norm, 219 additive, 12, 86–90, 104 additivity, 282, 283 adjusted R 2 , 78, 205, 206, 210–213 Advertising data set, 15, 16, 20, 59, 61–63, 68, 69, 71–76, 79, 81, 82, 87, 88, 102–104 agglomerative clustering, 390 Akaike information criterion, 78, 205, 206, 210–213 alternative hypothesis, 67 analysis of variance, 290 area under the curve, 147 argument, 42 AUC, 147 Auto data set, 14, 48, 49, 56, 90–93, 121, 122, 171, 176–178, 180, 182, 191, 193–195, 299, 371 backfitting, 284, 300 backward stepwise selection, 79, 208–209, 247 bagging, 12, 26, 303, 316–319, 328–330 baseline, 86 basis function, 270, 273 Bayes classifier, 37–40, 139 decision boundary, 140 error, 37–40 Bayes’ theorem, 138, 139, 226 Bayesian, 226–227 Bayesian information criterion, 78, 205, 206, 210–213 best subset selection, 205, 221, 244–247 bias, 33–36, 65, 82 bias-variance decomposition, 34 trade-off, 33–37, 42, 105, 149, 217, 230, 239, 243, 278, 307, 347, 357 binary, 28, 130 biplot, 377, 378 G. James et al., An Introduction to Statistical Learning: with Applications in R, Springer Texts in Statistics 103, DOI 10.1007/978-1-4614-7138-7, © Springer Science+Business Media New York 2013 419
420 Index Boolean, 159 boosting, 12, 25, 26, 303, 316, 321–324, 330–331 bootstrap, 12, 175, 187–190, 316 Boston data set, 14, 56, 110, 113, 126, 173, 201, 264, 299, 327, 328, 330, 333 bottom-up clustering, 390 boxplot, 50 branch, 305 Caravan data set, 14, 164, 335 Carseats data set, 14, 117, 123, 324, 333 categorical, 3, 28 classification, 3, 12, 28–29, 37–42, 127–173, 337–353 error rate, 311 tree, 311–314, 324–327 classifier, 127 cluster analysis, 26–28 clustering, 4, 26–28, 385–401 K-means, 12, 386–389 agglomerative, 390 bottom-up, 390 hierarchical, 386, 390–401 coefficient, 61 College data set, 14, 54, 263, 300 collinearity, 99–103 conditional probability, 37 confidence interval, 66–67, 81, 82, 103, 268 confounding, 136 confusion matrix, 145, 158 continuous, 3 contour plot, 46 contrast, 86 correlation, 70, 74, 396 Credit data set, 83, 84, 86, 89, 90, 99–102 cross-entropy, 311–312, 332 cross-validation, 12, 33, 36, 175–186, 205, 227, 248–251 k-fold, 181–184 leave-one-out, 178–181 curse of dimensionality, 108, 168, 242–243 data frame, 48 Data sets Advertising, 15, 16, 20, 59, 61–63, 68, 69, 71–76, 79, 81, 82, 87, 88, 102–104 Auto, 14, 48, 49, 56, 90–93, 121, 122, 171, 176–178, 180, 182, 191, 193–195, 299, 371 Boston, 14, 56, 110, 113, 126, 173, 201, 264, 299, 327, 328, 330, 333 Caravan, 14, 164, 335 Carseats, 14, 117, 123, 324, 333 College, 14, 54, 263, 300 Credit, 83, 84, 86, 89, 90, 99–102 Default, 14, 128–137, 144–148, 198, 199 Heart, 312, 313, 317–320, 354, 355 Hitters, 14, 244, 251, 255, 256, 304, 305, 310, 311, 334 Income, 16–18, 22–24 Khan, 14, 366 NCI60, 4, 5, 14, 407, 409–412 OJ, 14, 334, 371 Portfolio, 14, 194 Smarket, 3, 14, 154, 161, 162, 171 USArrests, 14, 377, 378, 381–383
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Springer Texts in Statistics Gareth
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Gareth James • Daniela Witten •
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To our parents: Alison and Michael
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viii Preface disciplines who wish t
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x Contents 3 Linear Regression 59 3
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3 Linear Regression This chapter is
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3.1 Simple Linear Regression 3.1 Si
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3.6 Lab: Linear Regression 109 p=1
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3.6 Lab: Linear Regression 111 Coef
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4 Classification The linear regress
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4.2 Why Not Linear Regression? 129
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4.3 Logistic Regression 131 0 500 1
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4.3 Logistic Regression 133 β 1 do
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4.3 Logistic Regression 135 Coeffic
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4.3 Logistic Regression 137 Default
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4.5 A Comparison of Classification
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176 5. Resampling Methods 5.1 Cross
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178 5. Resampling Methods Mean Squa
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180 5. Resampling Methods LOOCV 10
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182 5. Resampling Methods Mean Squa
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184 5. Resampling Methods has highe
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186 5. Resampling Methods Error Rat
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6.1 Subset Selection 211 C p 10000
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6.2 Shrinkage Methods 217 regressio
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6.2 Shrinkage Methods 223 Mean Squa
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7 Moving Beyond Linearity So far in
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7.4 Regression Splines 273 plot is
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7.4 Regression Splines 275 Natural
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7.5 Smoothing Splines 277 Wage 50 1
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7.5 Smoothing Splines 279 to the nu
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7.8.2 Splines 7.8 Lab: Non-linear M
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304 8. Tree-Based Methods Years < 4
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332 8. Tree-Based Methods 8.4 Exerc
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9 Support Vector Machines In this c
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9.1 Maximal Margin Classifier 339 X
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9.1 Maximal Margin Classifier 341 h
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9.1 Maximal Margin Classifier 343 m
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9.2 Support Vector Classifiers 345
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9.3 Support Vector Machines 349 X2
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9.3 Support Vector Machines 351 in
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9.3 Support Vector Machines 353 X2
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9.4 SVMs with More than Two Classes
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9.5 Relationship to Logistic Regres
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9.6 Lab: Support Vector Machines 35
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Page 438 and 439: Index 423 noise, 22, 228 non-linear
Page 440 and 441: Index 425 read.table(), 48, 49 regs
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